The R Language

R is a scripting language and a very powerful tool for data analysis and presentation, primarily due to the huge user base and their dedication to developing free and open source libraries/packages covering a vast range of different knowledge domains:

• Regression
• Machine Learning
• Image Analysis
• Network Analysis

The Comprehensive R Archive Network (CRAN) is the canonical repository for R packages, note that almost all* packages hosted on CRAN may be used within a Shiny app.

*Packages dependent on parallel or distributed computing are unlikely to be supported, contact shinyapps-support@rstudio.com with any questions

Learning R

There are thousands of online resources for learning R, many are available for free.

Two I’d like to personally endorse are:

• Datacamp.com provides an excellent free introductory course to the R language, I thoroughly recommend it with one caveat - skip the section on matrices.

• Lynda.com (and LinkedIn Learning) is a subscription learning platform that covers a vast range of topics. I’m a Lynda.com author and am contracted to produce introductory courses for R, you can get a free 3-month trial for Lynda here XXXX

The R Console

R is the name of the programming language and console within which many users of R write and evaluate their code.

To use R on your local machine you must download and install the R Console, it’s available on Windows, OS X and Linux.

Like all consoles, this application provides [only] the following functionality:

• Write code and script files
• Evaluate code and script files

RStudio is a free, open-source IDE (integrated development environment) that provides an extremely powerful and friendly interface for developing with R.

IDEs make it easier to manage your programming, providing the following features:

• Interactive overview of your variables and datasets
• Interactive file explorer for managing file dependencies
• Tabbed script interface, allowing code to be split into smaller, more manageable files
• Integrated documentation for the primary language of the IDE

RStudio, however, provides much more exciting features on top of a standard IDE:

• Integrated visualisation previews, including interactive htmlwidgets
• Support for creating, previewing and deploying interactive Shiny apps
• A powerful literate programming interface through RMarkdown*
• Tools for developing R packages

*more on RMarkdown after some actual code.

Base R and R Packages

When R is installed on your computer the machinery necessary to run R code is added to your computer and a number of “base” packages including; stats, utils and graphics.

See stackoverflow.com/a/9705725/1659890 for further details.

These packages will not get you far in life, unless you’re prepared to write a lot of code from scratch.

But you can guarantee* that any code samples you see online referring to “base R” only will work without having to install additional libraries.

Installing Packages

If a package is on CRAN then it is “installed” onto your using the following code, you’re advised to write this directly into the console and not into your documents

install.packages("ggplot2")

library(ggplot2)

Warning on Packages

While packages are incredibly useful, it is important not to offload all thought/development to packages for three important reasons:

• The package might be written poorly (unoptimised, or with incorrect assumptions)
• The package may well become unsupported in the future if the developer does not maintain it
• Not all packages are designed to play nicely with one another, manipulating your code to play nice with obnoxiousR might be harder than simple writing in base R

RStudio-backed Packages

• The “tidyverse” is a collection of packages maintained by RStudio devs [particularly Hadley Wickham]
• tidyverse packages play extremely nicely together
• tidyverse packages are extremely useful for preparing data for interactive visualisations
• tidyverse packages are highly optimised, often specifically around nitpicky details of bse R (readr is a good example of this)
• tidyverse is the backbone of the recently published, free online book R for Data Science

Tidyverse package workflow

• Import with readr
• Reshape with tidyr
• Filter, modify and query with dplyr
• Visualise with ggplot (but that’s not interactive…)

RStudio has links to a fantastic cheatsheet on the tidyverse (the complicated reshaping/filtering part of it) available under Help > Cheatsheets

Installing the tidyverse

There are currently over 15 packages in the tidyverse, it’s a pain installing each of them separarely. So RStudio have made everything easy to manage via the tidyverse package:

# install.packages("tidyverse")
library("tidyverse")

## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr

## Conflicts with tidy packages ----------------------------------------------

## filter(): dplyr, stats
## lag():    dplyr, stats

tidyverse_packages()

##  [1] "broom"     "dplyr"     "forcats"   "ggplot2"   "haven"    
##  [6] "httr"      "hms"       "jsonlite"  "lubridate" "magrittr" 
## [11] "modelr"    "purrr"     "readr"     "readxl"    "stringr"  
## [16] "tibble"    "rvest"     "tidyr"     "xml2"      "tidyverse"

R Syntax Catch-up

We’re going to be using what to some users is considered advanced R programming during today’s session, but often experienced R users get tripped up over brackets. It’s good to cement into your head what each bracket is for so that when you read code you know what’s going on:

• Round brackets ()

• Square brackets []

• Braces {}

rep(
  "strings",
  {
    no1 <- 2
    no1 +3
  }
)

## [1] "strings" "strings" "strings" "strings" "strings"